RegPattern2Vec: Link Prediction in Knowledge Graphs

Link prediction is an important task in many domains, including health sciences, biology, recommender systems, social networks, and many more. It is one of the problems residing within the intersection of knowledge graphs and machine learning. Link prediction aims to discover unknown links between entities in a graph using various techniques. However, due to the size of knowledge graphs today and their complexity, it is a challenging and time-consuming task. In this work, we present RegPattern2Vec, a method to effectively sample a large knowledge graph to learn node embeddings, while capturing the semantic relationships between graph nodes with minimum prior knowledge and human involvement. Our results show that the link prediction using RegPattern2Vec outperforms related graph embedding approaches on large-scale and complex knowledge graphs.